We lie for all kinds of reasons – we like to make ourselves feel better about our diet, “This key lime yogurt is just as good as a piece of pie.” We lie to protect the feelings of others, “Of course you haven’t put on any weight; you look great!” We also lie to cover up a mistake “I had nothing to do with that.”

There are many other types of lies cited in the article as well – avoidance, altruistic, and malicious lies to name only a few. With honest reflection on our own lives, it’s easy to see the many lies that we tell others for a wide array of reasons, but also, and perhaps more strikingly, the myriad of untruths that we tell ourselves. It has become a reflexive, human technique for dealing with our reality, something we do socially, professionally, and personally, and often without even realizing that we’re doing it.

We live in a world full of data. Television shows succeed or fail based on ratings. Sports fans and pundits gather statistical information to judge the ability and performance of players and teams. Gamblers and bookies are always calculating odds. Coupon cutters tabulate how much money they can save.

In the world of research and development, the sheer volume of data at play is staggering. So, when it comes to data management and quality, are we again burying our heads in the sand and lying to ourselves about the efficacy of our system? Is the data that drives our workflow really as good as it could be?

Economic advantage: enjoying the financial benefits

“Yes, honey. The trunk is full of shopping bags, but I got all of these items dirt cheap!”

We all enjoy the thrill of a bargain. We feel good about finding an item that costs so much less than it usually would, but at what cost? Have we accepted poorer quality resulting in the eventual need for replacement? Did we choose a model without the bells and whistles that we think we don’t need at the time, only to find out that we really do need those utilities down the road? What we believed to be financial gain in the moment can often result in financial loss later on—not just in dollars, but also in the time it takes to find and replace the product you chose in the first place.

As with everything in modern society, there are many options for R&D data management—and some of those options might even be free. You can already hear that voice in your head, or perhaps it’s the words of your boss or the accounting department: “There’s really no need to spend money on this at all, is there?” Remember those little lies of the moment, there’s always a reason that some things are free…

When data is saved on my computer in a folder structure that makes sense to me, it’s impossible for anyone else to know the data is there, let alone search for it and find it.

What if that same data on my computer is essential to the analysis being conducted by others? What if the data on my computer changes the analysis completely? Or worse, what if the analysis depends on that data, and without access others are left to duplicate my efforts and gather that data on their own?

Time wasted doubling the cost to produce the same result. This could all have been avoided had I only used a tool that would allow my team to know that the data already exists and exactly where to find it.

Self-impression: shaping a positive image of ourselves

“I don’t need a Fitbit to tell me I’m a calorie burning machine!”

We’ve all been in a position where we think we’re doing something very well until we are put to the test. Only then do we find out that we were either burying our head in the sand or overtly lying to ourselves about how good a job we were doing.

It’s easy to make assumptions that everyone within an organization is recording all of the necessary data required for someone else to understand and interpret what he or she has done to produce that result. It’s also easy to assume that they have each used the correct processes and followed the necessary regulations to exaction. But we all know where blind assumptions lead in the workplace.

It’s not just important to know that data exists and where to find it. It’s equally important to know that the data was captured in a way that others can understand and rely on—validity can never be in question and regulatory standards can never be fudged.

In the lab, if the data is invalid or difficult to understand, it can result in repeated work, costing an organization both time and money. But with the right laboratory data management software, checks and balances can be put into place ensuring that all necessary information is captured appropriately, and that data is produced according to pre-set business rules.